Summary of NANIA in
general:

NANIA is a
collaborative project aimed at
finding efficient ways to apply computation and rigorous analysis to
complex
systems in fields such as ecology, earthquakes, epidemics and social
science. It is funded for 4 years as a network of
researchers by the EPSRC as part of its Novel Computation
Initiative. The Funded Institutions are the universities of Edinburgh; Manchester; Herriot-Watt; and Manchester Metropolitan (here).

We will
develop computer models
which represent complex systems and can be validated by comparison with
experimental data collected by our collaborators.These
models are based on “autonomes” which
represent individuals (animals, species, genes, grains of rock, people)
which
interact via networks which represent their connections (spatial
adjacency,
predator-prey relationship, gene in same phonotype, friendship or kin).The system is the subject to some
external
driving (food resource, changing environment, applied stress).

The dynamics
of these systems typically
involves evolution of both the autonomes and the networks until a
“steady
state” is reached – by which we mean that although the autonomes and
network
may continue to evolve, certain global features (number of animals,
mean connectivity,
total use of resource, temperature, plate movement) reach constant
values when
averages over time.The nature of these
steady states is unknown, as are the general condition which cause them
as
opposed to a collapse of the system.Indeed, even defining global properties which encapsulate the
complexity
of the system without describing every detail is problematic and
ambiguous.

NANIA involves a twin track
approach. Specific researchers will make progress in building
models of specific systems: food webs, ecologies, earthquakes, gene
flow and social organisation. This work will illuminate those
areas, and find important results specific to those fields. The
second, collaborative track will involve pooling our computational
expertise to produce efficient and novel ways of solving the problems
and analysing our results in the more abstract framework of
coarse-graining the complexity to find the common principles which
govern the systems. The NANIA collaboration extends beyond
the work funded in this proposal: other work not funded here will
contribute to the second track through a series of workshops, and
we adopt an open policy of extending our modelling expertise to other
areas where autonome modelling has yet to make an impact.

Summary of NANIA at the
CPM:

Social systems are a rich source for
abstract models of coordination and control that have more general
application. Where we study conditions that facilitate the
spontaneous specialisation of skill and function, and mechanisms for
the emergence of groups. Specialisation of autonomes
(individuals) allows all aspects of an environment or problem to be
exploited in parallel with the minimum of competition – an attractive
paradigm for novel computing, particularly if it can emerge from
homogeneous underlying hardware. In ecology the driving force is
biological evolution, whilst in social systems the process can be the
result of coordination between the individuals. Specialisation
and group formation are most effective when they occur together since
they are mutually reinforcing: one species creates a niche for
the other. The formation of cooperative groups makes it easier
for members to develop specialise skills because they can rely upon
others in the group to provide for their other needs.

If benefits do not require individual
sacrifice, group formation is stable, but in other cases the
possibility of “cheats” taking the benefit without making the sacrifice
can make this unstable. Social mechanisms that stabilise groups against
“cheats” include: kin-based preference, reputation, tag-based
recognition and institutional mechanisms (e.g. contracts).

We will construct of a series of related
simulations and mathematical models at several different levels of
abstraction based on the high level SDML language.
Individual-based simulations will be constructed based on observed
social mechanisms. The exploration of these will suggest more
abstract simulations that focus on possible the key mechanisms involved
(e.g. tag-based group formation). Different "regions" of emergent
behaviour in the abstract simulations will be "mapped out", and further
mathematical approximations will be drawn up based upon inspection of
these behaviour. Once a reasonable understanding of the seperate
mechanisms has been achieved abstract mechanisms will be combined in
pairs to explore some of the "synergies" and "competition" between them.